Service
Custom AI agents for support, research and internal tools
A custom AI agent handles a whole task, such as answering tier-1 support tickets or researching accounts, using your own data and tools. For one DTC brand, an agent now resolves 70% of tier-1 tickets, and first response dropped from 4 hours to under 2 minutes. Start with a free audit.
- Trigger
- AI step
- Person decides
- Your systems
- TriggerTicket arrives
- AI stepAgent reads it
- Your systemsOrder and shipment looked upShopify and shipping data
- AI stepRoutine ticket resolvedorder status, returns, address changes
Exception path Everything else goes to the team
- of tier-1 support tickets resolved by an AI agent
- 70%
- first response time
- 4 hrstounder 2 min
What can you automate with a custom AI agent?
- Support agents that resolve “where is my order”, returns and address changes from Shopify and shipping data, and hand everything else to your team.
- Research assistants that gather and summarize information on accounts, candidates or topics so your people start from a brief, not a blank page.
- Document Q&A over your own files, policies and past work, with answers that cite where they came from.
- Internal tools your engineers never get to: data enrichment, ticket routers, first-pass candidate matching.
What results has this produced?
| Ref. | Measure | Before | After | Client |
|---|---|---|---|---|
| E1.0 | of tier-1 support tickets resolved by an AI agent | — | 70% | DTC brand case study: of tier-1 support tickets resolved by an AI agent |
| E1.1 | first response time | 4 hrs | under 2 min | DTC brand case study: first response time |
| S1.0 | to build an internal AI research tool instead of hiring 2 engineers | — | 1 week | SaaS case study: to build an internal AI research tool instead of hiring 2 engineers |
| S2.0 | research assistant, data enrichment and ticket router, all live | — | 3 tools, 2 weeks | SaaS case study: research assistant, data enrichment and ticket router, all live |
| R3.0 | drop in time-to-submit with a first-pass matching agent | — | 60%+ | staffing case study: drop in time-to-submit with a first-pass matching agent |
See the DTC support agent case study, the internal research tool case study and three internal tools in two weeks.
- TriggerResearch question
- AI stepInformation gathered
- AI stepFirst-pass summary written
- Person decidesPeople review the output
- to build an internal AI research tool instead of hiring 2 engineers
- 1 week
- TriggerSupport ticket comes in
- AI stepTicket classified
- Your systemsSent to the right queue, with context
- research assistant, data enrichment and ticket router, all live
- 3 tools, 2 weeks
How is this different from Zapier, DIY or hiring?
Off-the-shelf chatbots answer from a help center. A custom agent can look up the order, check the policy, take the allowed action and log what it did, inside the limits you set.
| Measure | DIY no-code (Zapier, Make) | SaaS point tool | In-house hire | WorkflowPal |
|---|---|---|---|---|
| Time to first result | Fast for simple triggers; stalls on messy inputs and edge cases | Fast if the tool fits your process exactly | Recruiting, onboarding, then the build | First workflow usually live within a couple of weeks |
| Cost model | Low subscription, plus your team’s build and fix time | Per-seat or per-usage subscription, indefinitely | Salary and benefits, ongoing | Fixed price per scope, agreed before work starts |
| Who maintains it | Whoever built it, usually someone with another job | The vendor, on their roadmap | Your hire | You own it; optional support if you want it |
| Fits your exact process | Partly; AI steps and exceptions get brittle | Only if your process matches the product | Yes | Yes, built around how your team works today |
| Where it runs | The no-code platform | The vendor’s cloud | Your systems | Your accounts, on your existing tools |
Which tools do you integrate with?
Agents are only useful if they can see your data. We connect them to the systems you already run: Shopify, Gorgias and Zendesk for support; your CRM, docs and databases for research and Q&A; Slack or email for hand-offs to people.
How long does it take and what does it cost?
The first workflow is usually live within a couple of weeks. Every build is fixed price per scope, agreed before work starts, with no hourly billing. See the process and pricing model.
How do I get started?
Request a free workflow audit. Tell us which task you want an agent to take over. You get back a one-page plan: the 3 tasks worth automating first, hours saved, and what a build would cost. It takes about 15 minutes of your time, and there is no obligation to buy anything.
Prefer to talk first? Book a 30-minute call.
Frequently asked questions
What is the difference between an AI agent and a workflow automation?
A workflow automation follows the same steps every time, with AI used inside some steps. An agent decides which steps to take to finish a task, within limits you set. Most projects use both. Read the full comparison.
How do you stop an agent from making things up?
It answers from your data, not from memory, and it is limited to the actions you allow. When it is unsure or a request is out of scope, it hands over to a person with the context attached.
Will customers know they are talking to AI?
That is your call, and we recommend being upfront. The agent can introduce itself as an assistant and offer a person at any point.
Do you hand over the code?
Yes. It lives in your repository and accounts, with documentation your team can maintain.
Is our data used to train AI models?
No. What we build runs in your own accounts and uses AI providers’ business APIs, which do not train on your data by default. Steps that send something to a customer or change a record can be set to wait for a person’s approval.